CDS&E: Accelerating Astrophysical Insight at Scale with Likelihood-Free Inference

CDS

• 批准号: 2206744
• 负责人: Joshua Bloom
• 金额: $ 57.29万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206744&HistoricalAwards=false
• 关键词: CDS& Accelerating; Astrophysical; Insight; Scale

In future surveys to unprecedented depth and image fidelity, the discovery and classification of sources will, by necessity, be fully automated, in part by using machine learning. This project will develop novel algorithms and codebases to accelerate model-based inference in time-domain astrophysics. Inference is becoming a fundamental bottleneck, from both a computation and human cost perspective, and the parameter estimation task can be the main impediment to scientific progress. Often, domain experts are required to supervise fitting in order to narrow search spaces and speed up inference. This work will focus on likelihood-free inference (LFI) approaches using neural networks. It will create a new open-source LFI Python library, and carry out domain-specific inference tasks for gravitational microlensing and for eclipsing binary stars. The software will let non-experts use LFI with smaller computational penalties, and enable on-the-fly inference continuously during data collection. Running a multidisciplinary workshop and adding LFI into undergraduate and graduate courses will improve both the research infrastructure and STEM education.This approach means that the cost and time to compute forward models are covered during training, and then leveraged for parameter estimation on new data. This effort will a) create LFI architectures for irregularly sampled and noisy time-series data, b) advance methods for anomaly detection and posterior calibration, ensuring LFI parameter estimates are unbiased, and c) develop approaches for fast model selection. On-the-fly LFI can inform further data acquisition, and this virtuous feedback loop helps with more efficient use of expensive follow-up resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在未来对空前的深度和图像保真度的调查中，来源的发现和分类将有必要完全自动化，部分是通过使用机器学习的。 该项目将开发新颖的算法和代码库，以加速基于模型的天体物理学的推断。 从计算和人为成本的角度来看，推论正成为一种基本的瓶颈，参数估计任务可能是科学进步的主要障碍。 通常，域专家需要监督拟合才能缩小搜索空间并加快推理的速度。 这项工作将集中于使用神经网络的无似然推理（LFI）方法。 它将创建一个新的开源LFI Python库，并执行针对重力微透析和磨损二进制恒星的域特异性推理任务。 该软件将使非专家使用LFI和较小的计算惩罚，并在数据收集过程中连续启用推理。 进行多学科研讨会并将LFI添加到本科和研究生课程中将改善研究基础设施和STEM教育。这种方法意味着在培训期间涵盖了计算远期模型的成本和时间，然后利用了新数据的参数估算。这项工作将a）为不规则采样和嘈杂的时间序列数据创建LFI体系结构，b）用于异常检测和后校准的提前方法，确保LFI参数估计是公正的，c）开发用于快速模型选择的方法。 即时的LFI可以为进一步的数据获取提供信息，并且这种良性的反馈循环有助于更有效地利用昂贵的后续资源。该奖项反映了NSF的法定任务，并被认为是通过使用该基金会的知识分子优点和评估来获得支持的更广泛的影响审查标准。